Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations8839
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1018.6 KiB
Average record size in memory118.0 B

Variable types

Categorical1
Boolean6
Text1
Numeric9
DateTime2

Alerts

type has constant value "True" Constant
label is highly imbalanced (73.4%) Imbalance
site_admin is highly imbalanced (94.8%) Imbalance
public_repos is highly skewed (γ1 = 30.97124933) Skewed
public_gists is highly skewed (γ1 = 57.42774864) Skewed
following is highly skewed (γ1 = 31.14344449) Skewed
created_at has unique values Unique
public_repos has 203 (2.3%) zeros Zeros
public_gists has 2527 (28.6%) zeros Zeros
followers has 233 (2.6%) zeros Zeros
following has 1516 (17.2%) zeros Zeros
text_bot_count has 8513 (96.3%) zeros Zeros
log_public_repos has 203 (2.3%) zeros Zeros
log_public_gists has 2527 (28.6%) zeros Zeros
log_followers has 233 (2.6%) zeros Zeros
log_following has 1516 (17.2%) zeros Zeros

Reproduction

Analysis started2024-11-26 04:58:46.013816
Analysis finished2024-11-26 04:58:55.482993
Duration9.47 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

label
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.1 KiB
Human
8438 
Bot
 
401

Length

Max length5
Median length5
Mean length4.9092658
Min length3

Characters and Unicode

Total characters43393
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHuman
2nd rowHuman
3rd rowHuman
4th rowHuman
5th rowHuman

Common Values

ValueCountFrequency (%)
Human 8438
95.5%
Bot 401
 
4.5%

Length

2024-11-26T12:58:55.647325image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T12:58:55.768941image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
human 8438
95.5%
bot 401
 
4.5%

Most occurring characters

ValueCountFrequency (%)
H 8438
19.4%
u 8438
19.4%
m 8438
19.4%
a 8438
19.4%
n 8438
19.4%
B 401
 
0.9%
o 401
 
0.9%
t 401
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 8438
19.4%
u 8438
19.4%
m 8438
19.4%
a 8438
19.4%
n 8438
19.4%
B 401
 
0.9%
o 401
 
0.9%
t 401
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 8438
19.4%
u 8438
19.4%
m 8438
19.4%
a 8438
19.4%
n 8438
19.4%
B 401
 
0.9%
o 401
 
0.9%
t 401
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 8438
19.4%
u 8438
19.4%
m 8438
19.4%
a 8438
19.4%
n 8438
19.4%
B 401
 
0.9%
o 401
 
0.9%
t 401
 
0.9%

type
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
True
8839 
ValueCountFrequency (%)
True 8839
100.0%
2024-11-26T12:58:55.864017image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

site_admin
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
False
8787 
True
 
52
ValueCountFrequency (%)
False 8787
99.4%
True 52
 
0.6%
2024-11-26T12:58:56.062048image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

company
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
True
6168 
False
2671 
ValueCountFrequency (%)
True 6168
69.8%
False 2671
30.2%
2024-11-26T12:58:56.163939image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

blog
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
True
5553 
False
3286 
ValueCountFrequency (%)
True 5553
62.8%
False 3286
37.2%
2024-11-26T12:58:56.264130image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

location
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
True
7312 
False
1527 
ValueCountFrequency (%)
True 7312
82.7%
False 1527
 
17.3%
2024-11-26T12:58:56.364192image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

hireable
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
False
6632 
True
2207 
ValueCountFrequency (%)
False 6632
75.0%
True 2207
 
25.0%
2024-11-26T12:58:56.455844image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

bio
Text

Distinct8641
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:56.832843image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length160
Median length116
Mean length61.460459
Min length1

Characters and Unicode

Total characters543249
Distinct characters1746
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8574 ?
Unique (%)97.0%

Sample

1st rowI just press the buttons randomly, and the program evolves...
2nd rowTime is unimportant, only life important.
3rd rowDone studying. Need challenges.
4th rowAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.
5th rowSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.
ValueCountFrequency (%)
3069
 
3.9%
and 2526
 
3.2%
engineer 1583
 
2.0%
software 1521
 
1.9%
of 1488
 
1.9%
at 1380
 
1.8%
developer 1236
 
1.6%
the 1086
 
1.4%
a 1038
 
1.3%
i 1033
 
1.3%
Other values (14754) 62407
79.6%
2024-11-26T12:58:57.298202image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 543249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 543249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 543249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

public_repos
Real number (ℝ)

Skewed  Zeros 

Distinct594
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.67112
Minimum0
Maximum26360
Zeros203
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:57.430090image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122
median55
Q3114
95-th percentile319
Maximum26360
Range26360
Interquartile range (IQR)92

Descriptive statistics

Standard deviation588.34113
Coefficient of variation (CV)5.086327
Kurtosis1112.9756
Mean115.67112
Median Absolute Deviation (MAD)39
Skewness30.971249
Sum1022417
Variance346145.28
MonotonicityNot monotonic
2024-11-26T12:58:57.562389image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203
 
2.3%
1 123
 
1.4%
15 109
 
1.2%
14 106
 
1.2%
11 99
 
1.1%
19 98
 
1.1%
7 98
 
1.1%
24 96
 
1.1%
6 96
 
1.1%
8 95
 
1.1%
Other values (584) 7716
87.3%
ValueCountFrequency (%)
0 203
2.3%
1 123
1.4%
2 89
1.0%
3 80
 
0.9%
4 92
1.0%
5 81
 
0.9%
6 96
1.1%
7 98
1.1%
8 95
1.1%
9 91
1.0%
ValueCountFrequency (%)
26360 1
< 0.1%
22618 1
< 0.1%
20693 1
< 0.1%
17425 1
< 0.1%
16985 1
< 0.1%
16839 1
< 0.1%
9666 1
< 0.1%
9554 1
< 0.1%
6344 1
< 0.1%
6079 1
< 0.1%

public_gists
Real number (ℝ)

Skewed  Zeros 

Distinct305
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.434551
Minimum0
Maximum55781
Zeros2527
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:57.677474image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q317
95-th percentile90
Maximum55781
Range55781
Interquartile range (IQR)17

Descriptive statistics

Standard deviation881.83821
Coefficient of variation (CV)22.943892
Kurtosis3452.1685
Mean38.434551
Median Absolute Deviation (MAD)4
Skewness57.427749
Sum339723
Variance777638.63
MonotonicityNot monotonic
2024-11-26T12:58:57.815664image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2527
28.6%
1 801
 
9.1%
2 545
 
6.2%
3 390
 
4.4%
4 324
 
3.7%
5 318
 
3.6%
6 277
 
3.1%
7 202
 
2.3%
9 194
 
2.2%
8 169
 
1.9%
Other values (295) 3092
35.0%
ValueCountFrequency (%)
0 2527
28.6%
1 801
 
9.1%
2 545
 
6.2%
3 390
 
4.4%
4 324
 
3.7%
5 318
 
3.6%
6 277
 
3.1%
7 202
 
2.3%
8 169
 
1.9%
9 194
 
2.2%
ValueCountFrequency (%)
55781 1
< 0.1%
53660 1
< 0.1%
26879 1
< 0.1%
10604 1
< 0.1%
3450 1
< 0.1%
1750 1
< 0.1%
1679 1
< 0.1%
1611 1
< 0.1%
1513 1
< 0.1%
1337 1
< 0.1%

followers
Real number (ℝ)

Zeros 

Distinct1405
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.20251
Minimum0
Maximum58452
Zeros233
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:58.016085image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q122
median73
Q3235
95-th percentile1470.9
Maximum58452
Range58452
Interquartile range (IQR)213

Descriptive statistics

Standard deviation1522.4168
Coefficient of variation (CV)4.004226
Kurtosis362.87009
Mean380.20251
Median Absolute Deviation (MAD)64
Skewness15.059094
Sum3360610
Variance2317752.9
MonotonicityNot monotonic
2024-11-26T12:58:58.166574image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 233
 
2.6%
1 146
 
1.7%
2 128
 
1.4%
4 118
 
1.3%
3 116
 
1.3%
9 111
 
1.3%
6 110
 
1.2%
7 104
 
1.2%
5 98
 
1.1%
16 97
 
1.1%
Other values (1395) 7578
85.7%
ValueCountFrequency (%)
0 233
2.6%
1 146
1.7%
2 128
1.4%
3 116
1.3%
4 118
1.3%
5 98
1.1%
6 110
1.2%
7 104
1.2%
8 92
 
1.0%
9 111
1.3%
ValueCountFrequency (%)
58452 1
< 0.1%
31120 1
< 0.1%
29719 1
< 0.1%
29414 1
< 0.1%
28411 1
< 0.1%
25815 1
< 0.1%
24893 1
< 0.1%
22707 1
< 0.1%
22520 1
< 0.1%
21057 1
< 0.1%

following
Real number (ℝ)

Skewed  Zeros 

Distinct555
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.67847
Minimum0
Maximum27775
Zeros1516
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:58.296402image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q344
95-th percentile242.1
Maximum27775
Range27775
Interquartile range (IQR)42

Descriptive statistics

Standard deviation508.36665
Coefficient of variation (CV)6.8074057
Kurtosis1321.4193
Mean74.67847
Median Absolute Deviation (MAD)12
Skewness31.143444
Sum660083
Variance258436.65
MonotonicityNot monotonic
2024-11-26T12:58:58.434731image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1516
 
17.2%
1 581
 
6.6%
2 398
 
4.5%
3 299
 
3.4%
4 272
 
3.1%
5 238
 
2.7%
6 229
 
2.6%
8 191
 
2.2%
7 179
 
2.0%
9 168
 
1.9%
Other values (545) 4768
53.9%
ValueCountFrequency (%)
0 1516
17.2%
1 581
 
6.6%
2 398
 
4.5%
3 299
 
3.4%
4 272
 
3.1%
5 238
 
2.7%
6 229
 
2.6%
7 179
 
2.0%
8 191
 
2.2%
9 168
 
1.9%
ValueCountFrequency (%)
27775 1
< 0.1%
16741 1
< 0.1%
15931 1
< 0.1%
10268 1
< 0.1%
9720 1
< 0.1%
9686 1
< 0.1%
9532 1
< 0.1%
9367 1
< 0.1%
7374 1
< 0.1%
5879 1
< 0.1%

created_at
Date

Unique 

Distinct8839
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size138.1 KiB
Minimum2008-01-27 07:09:47+00:00
Maximum2021-12-05 22:58:37+00:00
2024-11-26T12:58:58.565999image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:58.710130image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct8805
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size138.1 KiB
Minimum2018-08-06 22:55:54+00:00
Maximum2023-10-14 14:33:48+00:00
2024-11-26T12:58:58.832545image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:58.982805image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

text_bot_count
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.068786062
Minimum0
Maximum5
Zeros8513
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:59.095800image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39259064
Coefficient of variation (CV)5.7074156
Kurtosis50.063152
Mean0.068786062
Median Absolute Deviation (MAD)0
Skewness6.6884455
Sum608
Variance0.15412741
MonotonicityNot monotonic
2024-11-26T12:58:59.182872image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8513
96.3%
1 137
 
1.5%
2 114
 
1.3%
3 62
 
0.7%
4 8
 
0.1%
5 5
 
0.1%
ValueCountFrequency (%)
0 8513
96.3%
1 137
 
1.5%
2 114
 
1.3%
3 62
 
0.7%
4 8
 
0.1%
5 5
 
0.1%
ValueCountFrequency (%)
5 5
 
0.1%
4 8
 
0.1%
3 62
 
0.7%
2 114
 
1.3%
1 137
 
1.5%
0 8513
96.3%

log_public_repos
Real number (ℝ)

Zeros 

Distinct594
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8694923
Minimum0
Maximum10.179641
Zeros203
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:59.332351image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3862944
Q13.1354942
median4.0253517
Q34.7449321
95-th percentile5.768321
Maximum10.179641
Range10.179641
Interquartile range (IQR)1.6094379

Descriptive statistics

Standard deviation1.3322031
Coefficient of variation (CV)0.34428369
Kurtosis1.0391104
Mean3.8694923
Median Absolute Deviation (MAD)0.78683266
Skewness-0.52674041
Sum34202.442
Variance1.774765
MonotonicityNot monotonic
2024-11-26T12:58:59.470862image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203
 
2.3%
0.6931471806 123
 
1.4%
2.772588722 109
 
1.2%
2.708050201 106
 
1.2%
2.48490665 99
 
1.1%
2.995732274 98
 
1.1%
2.079441542 98
 
1.1%
3.218875825 96
 
1.1%
1.945910149 96
 
1.1%
2.197224577 95
 
1.1%
Other values (584) 7716
87.3%
ValueCountFrequency (%)
0 203
2.3%
0.6931471806 123
1.4%
1.098612289 89
1.0%
1.386294361 80
 
0.9%
1.609437912 92
1.0%
1.791759469 81
 
0.9%
1.945910149 96
1.1%
2.079441542 98
1.1%
2.197224577 95
1.1%
2.302585093 91
1.0%
ValueCountFrequency (%)
10.17964092 1
< 0.1%
10.02654554 1
< 0.1%
9.937599082 1
< 0.1%
9.765718623 1
< 0.1%
9.740144754 1
< 0.1%
9.731512288 1
< 0.1%
9.176473302 1
< 0.1%
9.164819857 1
< 0.1%
8.75542238 1
< 0.1%
8.712759975 1
< 0.1%

log_public_gists
Real number (ℝ)

Zeros 

Distinct305
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7520114
Minimum0
Maximum10.929207
Zeros2527
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:59.596712image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.6094379
Q32.8903718
95-th percentile4.5108595
Maximum10.929207
Range10.929207
Interquartile range (IQR)2.8903718

Descriptive statistics

Standard deviation1.5616003
Coefficient of variation (CV)0.89131858
Kurtosis-0.14700818
Mean1.7520114
Median Absolute Deviation (MAD)1.4816045
Skewness0.61543332
Sum15486.029
Variance2.4385955
MonotonicityNot monotonic
2024-11-26T12:58:59.733107image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2527
28.6%
0.6931471806 801
 
9.1%
1.098612289 545
 
6.2%
1.386294361 390
 
4.4%
1.609437912 324
 
3.7%
1.791759469 318
 
3.6%
1.945910149 277
 
3.1%
2.079441542 202
 
2.3%
2.302585093 194
 
2.2%
2.197224577 169
 
1.9%
Other values (295) 3092
35.0%
ValueCountFrequency (%)
0 2527
28.6%
0.6931471806 801
 
9.1%
1.098612289 545
 
6.2%
1.386294361 390
 
4.4%
1.609437912 324
 
3.7%
1.791759469 318
 
3.6%
1.945910149 277
 
3.1%
2.079441542 202
 
2.3%
2.197224577 169
 
1.9%
2.302585093 194
 
2.2%
ValueCountFrequency (%)
10.92920652 1
< 0.1%
10.89044176 1
< 0.1%
10.19913779 1
< 0.1%
9.269080867 1
< 0.1%
8.146419323 1
< 0.1%
7.467942332 1
< 0.1%
7.426549072 1
< 0.1%
7.385230923 1
< 0.1%
7.322510434 1
< 0.1%
7.198931241 1
< 0.1%

log_followers
Real number (ℝ)

Zeros 

Distinct1405
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2764705
Minimum0
Maximum10.975978
Zeros233
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:58:59.864227image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0986123
Q13.1354942
median4.3040651
Q35.4638318
95-th percentile7.2943019
Maximum10.975978
Range10.975978
Interquartile range (IQR)2.3283376

Descriptive statistics

Standard deviation1.824143
Coefficient of variation (CV)0.42655339
Kurtosis0.043933647
Mean4.2764705
Median Absolute Deviation (MAD)1.1685709
Skewness-0.023421046
Sum37799.723
Variance3.3274977
MonotonicityNot monotonic
2024-11-26T12:58:59.996572image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 233
 
2.6%
0.6931471806 146
 
1.7%
1.098612289 128
 
1.4%
1.609437912 118
 
1.3%
1.386294361 116
 
1.3%
2.302585093 111
 
1.3%
1.945910149 110
 
1.2%
2.079441542 104
 
1.2%
1.791759469 98
 
1.1%
2.833213344 97
 
1.1%
Other values (1395) 7578
85.7%
ValueCountFrequency (%)
0 233
2.6%
0.6931471806 146
1.7%
1.098612289 128
1.4%
1.386294361 116
1.3%
1.609437912 118
1.3%
1.791759469 98
1.1%
1.945910149 110
1.2%
2.079441542 104
1.2%
2.197224577 92
 
1.0%
2.302585093 111
1.3%
ValueCountFrequency (%)
10.97597829 1
< 0.1%
10.34563811 1
< 0.1%
10.2995755 1
< 0.1%
10.28926003 1
< 0.1%
10.25456687 1
< 0.1%
10.15874973 1
< 0.1%
10.12238209 1
< 0.1%
10.03047256 1
< 0.1%
10.02220349 1
< 0.1%
9.955035814 1
< 0.1%

log_following
Real number (ℝ)

Zeros 

Distinct555
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5342293
Minimum0
Maximum10.231928
Zeros1516
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size138.1 KiB
2024-11-26T12:59:00.127496image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0986123
median2.5649494
Q33.8066625
95-th percentile5.4934721
Maximum10.231928
Range10.231928
Interquartile range (IQR)2.7080502

Descriptive statistics

Standard deviation1.7952236
Coefficient of variation (CV)0.70839036
Kurtosis-0.50414565
Mean2.5342293
Median Absolute Deviation (MAD)1.4423838
Skewness0.27163164
Sum22400.053
Variance3.2228277
MonotonicityNot monotonic
2024-11-26T12:59:00.428180image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1516
 
17.2%
0.6931471806 581
 
6.6%
1.098612289 398
 
4.5%
1.386294361 299
 
3.4%
1.609437912 272
 
3.1%
1.791759469 238
 
2.7%
1.945910149 229
 
2.6%
2.197224577 191
 
2.2%
2.079441542 179
 
2.0%
2.302585093 168
 
1.9%
Other values (545) 4768
53.9%
ValueCountFrequency (%)
0 1516
17.2%
0.6931471806 581
 
6.6%
1.098612289 398
 
4.5%
1.386294361 299
 
3.4%
1.609437912 272
 
3.1%
1.791759469 238
 
2.7%
1.945910149 229
 
2.6%
2.079441542 179
 
2.0%
2.197224577 191
 
2.2%
2.302585093 168
 
1.9%
ValueCountFrequency (%)
10.23192762 1
< 0.1%
9.725675811 1
< 0.1%
9.676084944 1
< 0.1%
9.236884927 1
< 0.1%
9.182043773 1
< 0.1%
9.178540059 1
< 0.1%
9.162514742 1
< 0.1%
9.145054905 1
< 0.1%
8.905851181 1
< 0.1%
8.679312041 1
< 0.1%

Interactions

2024-11-26T12:58:54.190606image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:46.616107image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.659878image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.617002image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.600918image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.449385image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.317325image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.270675image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.249754image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.287946image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:46.732874image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.752066image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.750756image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.700216image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.533048image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.402709image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.373472image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.368571image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.382915image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:46.821485image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.847101image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.849374image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.799135image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.632812image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.514411image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.466327image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.466638image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.470869image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.018627image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.949271image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.938819image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.886715image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.738051image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.615954image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.568638image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.599168image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.568272image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.166212image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.048757image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.032911image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.982590image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.829874image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.699861image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.683606image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.711965image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.671853image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.282997image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.161248image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.129503image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.069013image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.919424image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.796107image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.815518image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.803992image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.766269image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.381549image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.301003image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.226168image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.170217image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.014726image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.881584image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.915426image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.915971image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.865493image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.470066image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.420827image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.413768image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.271107image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.113705image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.983121image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.032068image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.997574image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.968438image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:47.552212image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:48.516646image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:49.507424image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:50.365143image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:51.214852image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:52.069324image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:53.133179image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-11-26T12:58:54.101313image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Missing values

2024-11-26T12:58:55.097807image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-26T12:58:55.373395image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
1HumanTrueFalseFalseTrueFalseTrueI just press the buttons randomly, and the program evolves...303962015-06-29 10:12:46+00:002023-10-07 06:26:14+00:0003.4339871.3862942.3025851.945910
2HumanTrueFalseTrueTrueTrueTrueTime is unimportant,\nonly life important.1034912122212008-08-29 16:20:03+00:002023-10-02 02:11:21+00:0004.6443913.9120237.1008525.402677
5HumanTrueFalseTrueTrueTrueFalseDone studying. Need challenges.5612272017-04-11 14:08:07+00:002023-10-11 05:59:26+00:0004.0430510.6931473.1354942.079442
6HumanTrueFalseTrueTrueTrueTrueAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.277113963162008-04-07 22:22:22+00:002023-09-27 09:04:56+00:0005.6276217.0387844.1588832.833213
7HumanTrueFalseTrueFalseTrueFalseSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.3712202012-01-19 21:57:07+00:002023-08-07 16:06:34+00:0003.6375860.6931473.1354940.000000
9HumanTrueFalseTrueTrueTrueFalseHi4291422013-07-23 23:29:34+00:002023-10-09 20:47:05+00:0003.7612002.3025852.7080501.098612
10HumanTrueFalseTrueFalseTrueFalse\n Software Engineer\n421313262016-04-05 07:29:09+00:002023-10-05 11:27:42+00:0003.7612002.6390572.6390573.295837
11HumanTrueFalseTrueFalseFalseFalseSenior Staff SWE on Open Source Security @ Google.\n\nFounder of the OSV.dev project, co-founder of OSS-Fuzz.47220842011-04-29 14:08:17+00:002023-10-14 02:56:25+00:0003.8712011.0986125.3423341.609438
12HumanTrueFalseTrueTrueTrueTrue👋 • Developer enjoying Cloud Infrastructure and Artificial Intelligence. Mathematics Student at Paris-Saclay20022322017-06-27 19:04:38+00:002023-09-22 12:01:52+00:0003.0445220.0000003.1354943.496508
13HumanTrueFalseTrueTrueTrueTrue👉Web Dev Freelance17234262018-01-02 17:08:06+00:002023-09-27 06:39:00+00:0002.8903721.0986123.5553483.295837
labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
19732BotTrueFalseFalseTrueTrueFalse🏗️ 👷‍♂️19018292015-02-21 19:10:54+00:002023-09-22 18:10:36+00:0002.9957320.0000002.9444393.401197
19737HumanTrueFalseTrueFalseFalseFalseBuilding the next generation data integration protocol!001812021-09-09 13:33:01+00:002023-10-07 12:18:34+00:0000.0000000.0000002.9444390.693147
19740HumanTrueFalseFalseFalseTrueFalseStep by step for last success.28018722013-04-27 06:59:03+00:002023-10-07 11:22:16+00:0003.3672960.0000005.2364421.098612
19747HumanTrueFalseFalseFalseFalseFalsephysics3203712013-07-10 16:55:44+00:002023-10-14 10:32:32+00:0003.4965080.0000003.6375860.693147
19749HumanTrueFalseTrueTrueTrueFalseSenior Software Engineer, WWW and beyond!681042272011-07-31 03:41:02+00:002023-10-10 14:27:48+00:0024.2341072.3978953.7612003.332205
19751HumanTrueFalseFalseTrueTrueTrueLinux Kernel developer at Qualcomm Innovation Center. Alum of Purdue University.32202142013-09-15 02:41:10+00:002023-03-13 02:43:39+00:0003.4965083.0445223.0910421.609438
19754BotTrueFalseTrueFalseTrueFalseSoftware engineer @intel63202018-05-31 02:26:59+00:002023-09-29 09:45:07+00:0001.9459101.3862941.0986120.000000
19760BotTrueFalseFalseFalseFalseFalseI am the bot account of @alvaroaleman10002018-12-15 19:55:31+00:002021-07-27 14:14:25+00:0020.6931470.0000000.0000000.000000
19763BotTrueFalseTrueTrueTrueFalseTony came to Linux in 1994 and has never looked back. His entire professional career has been spent working with or on Linux. First as a systems administrator36161142014-07-02 23:27:34+00:002023-08-15 16:38:34+00:0003.6109182.8332132.4849071.609438
19765HumanTrueFalseTrueFalseTrueFalseSoftware engineer at RealTracs.1301012015-11-14 14:44:05+00:002022-08-23 21:09:49+00:0002.6390570.0000002.3978950.693147